More and more people see hybrid renewable energy systems (HRES) that use solar photovoltaic (PV), wind, and battery storage as good ways to deal with problems with intermittent power and make sure that power is always available. This research assesses the techno-economic and reliability performance of a solar-wind-battery hybrid system optimized through Particle Swarm Optimization (PSO). Component models include temperature-adjusted PV output, wind turbine power curves, and a battery framework based on state of charge (SOC). The goal of optimization is to lower the levelized cost of energy (LCOE) while keeping reliability indices like energy not served (ENS) and loss of load probability (LOLP) in check. The PSO-based system cuts LCOE by 12-15% and ENS by up to 70% compared to heuristic baseline configurations, according to simulation results. The analysis of dispatch shows how important battery storage is for managing peak loads and smoothing out renewable energy sources. A sensitivity analysis shows that the cost of PV, the wind capacity factor, and the battery efficiency are the most important factors. This study emphasizes the potential of PSO in the design of efficient, cost-effective, and reliable hybrid renewable energy systems, while delineating avenues for future improvements.
Keywords
Hybrid Renewable Energy SystemsSolar-Wind-BatteryParticle Swarm OptimizationLcoeEnsReliabilityOptimization.
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